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Data Analysis Expressions (DAX) is a formula expression language used in Analysis Services, Power BI, and Power Pivot in Excel. DAX formulas include functions, operators, and values to perform advanced calculations and queries on data in related tables and columns in tabular data models.
This article provides only a basic introduction to the most important concepts in DAX. It describes DAX as it applies to all the products that use it. Some functionality may not apply to certain products or use cases. Refer to your product's documentation describing its particular implementation of DAX.
Measures
Measures are dynamic calculation formulas where the results change depending on context. Measures are used in reporting that support combining and filtering model data by using multiple attributes such as a Power BI report or Excel PivotTable or PivotChart. Measures are created by using the DAX formula bar in the model designer. A formula in a measure can use standard aggregation functions automatically created by using the Autosum feature, such as COUNT or SUM, or you can define your own formula by using the DAX formula bar. Named measures can be passed as an argument to other measures. When you define a formula for a measure in the formula bar, a Tooltip feature shows a preview of what the results would be for the total in the current context, but otherwise the results are not immediately output anywhere. The reason you cannot see the (filtered) results of the calculation immediately is because the result of a measure cannot be determined without context. To evaluate a measure requires a reporting client application that can provide the context needed to retrieve the data relevant to each cell and then evaluate the expression for each cell. That client might be an Excel PivotTable or PivotChart, a Power BI report, or a table expression in a DAX query in SQL Server Management Studio (SSMS). Regardless of the client, a separate query is run for each cell in the results. That is to say, each combination of row and column headers in a PivotTable, or each selection of slicers and filters in a Power BI report, generates a different subset of data over which the measure is calculated. For example, using this very simple measure formula: When a user places the TotalSales measure in a report, and then places the Product Category column from a Product table into Filters, the sum of Sales Amount is calculated and displayed for each product category. Unlike calculated columns, the syntax for a measure includes the measure's name preceding the formula. In the example just provided, the name Total Sales appears preceding the formula. After you've created a measure, the name and its definition appear in the reporting client application Fields list, and depending on perspectives and roles is available to all users of the model. To learn more, see: Measures in Power BI Desktop Measures in Analysis Services Measures in Power Pivot
Calculated columns
A calculated column is a column that you add to an existing table (in the model designer) and then create a DAX formula that defines the column's values. When a calculated column contains a valid DAX formula, values are calculated for each row as soon as the formula is entered. Values are then stored in the in-memory data model. For example, in a Date table, when the formula is entered into the formula bar: A value for each row in the table is calculated by taking values from the Calendar Year column (in the same Date table), adding a space and the capital letter Q, and then adding the values from the Calendar Quarter column (in the same Date table). The result for each row in the calculated column is calculated immediately and appears, for example, as 2017 Q1. Column values are only recalculated if the table or any related table is processed (refresh) or the model is unloaded from memory and then reloaded, like when closing and reopening a Power BI Desktop file. To learn more, see: Calculated columns in Power BI Desktop Calculated columns in Analysis Services Calculated Columns in Power Pivot.
Calculated tables
A calculated table is a computed object, based on a formula expression, derived from all or part of other tables in the same model. Instead of querying and loading values into your new table's columns from a data source, a DAX formula defines the table's values. Calculated tables can be helpful in a role-playing dimension. An example is the Date table, as OrderDate, ShipDate, or DueDate, depending on the foreign key relationship. By creating a calculated table for ShipDate explicitly, you get a standalone table that is available for queries, as fully operable as any other table. Calculated tables are also useful when configuring a filtered rowset, or a subset or superset of columns from other existing tables. This allows you to keep the original table intact while creating variations of that table to support specific scenarios. Calculated tables support relationships with other tables. The columns in your calculated table have data types, formatting, and can belong to a data category. Calculated tables can be named, and surfaced or hidden just like any other table. Calculated tables are re-calculated if any of the tables it pulls data from are refreshed or updated. To learn more, see: Calculated tables in Power BI Desktop Calculated tables in Analysis Services.
DAX queries can be created and run in SQL Server Management Studio (SSMS) and open-source tools like DAX Studio (daxstudio.org). Unlike DAX calculation formulas, which can only be created in tabular data models, DAX queries can also be run against Analysis Services Multidimensional models. DAX queries are often easier to write and more efficient than Multidimensional Data Expressions (MDX) queries.
A DAX query is a statement, similar to a SELECT statement in T-SQL. The most basic type of DAX query is an evaluate statement. For example,
Returns in Results a table listing only those products with a SafetyStockLevel less than 200, in ascending order by EnglishProductName.
You can create measures as part of the query. Measures exist only for the duration of the query. To learn more, see DAX queries.
DAX formulas are essential for creating calculations in calculated columns and measures, and securing your data by using row-level security. To create formulas for calculated columns and measures, use the formula bar along the top of the model designer window or the DAX Editor. To create formulas for row-level security, use the Role Manager or Mana...
A function is a named formula within an expression. Most functions have required and optional arguments, also known as parameters, as input. When the function is executed, a value is returned. DAX includes functions you can use to perform calculations using dates and times, create conditional values, work with strings, perform lookups based on relationships, and the ability to iterate over a table to perform recursive calculations. If you are familiar with Excel formulas, many of these functions will appear very similar; however, DAX formulas are different in the following important ways:
•A DAX function always references a complete column or a table. If you want to use only particular values from a table or column, you can add filters to the formula.
•If you need to customize calculations on a row-by-row basis, DAX provides functions that let you use the current row value or a related value as a kind of parameter, to perform calculations that vary by context. To understand how these functions work, see Context in this article.
•DAX includes many functions that return a table, rather than a value. The table is not displayed in a reporting client, but is used to provide input to other functions. For example, you can retrieve a table and then count the distinct values in it, or calculate dynamic sums across filtered tables or columns.
You can create variables within an expression by using VAR. VAR is technically not a function, it's a keyword to store the result of an expression as a named variable. That variable can then be passed as an argument to other measure expressions. For example:
In this example, TotalQty can be passed as a named variable to other expressions. Variables can be of any scalar data type, including tables. Using variables in your DAX formulas can be incredibly powerful.
You can import data into a model from many different data sources that might support different data types. When you import data into a model, the data is converted to one of the tabular model data types. When the model data is used in a calculation, the data is then converted to a DAX data type for the duration and output of the calculation. When you create a DAX formula, the terms used in the formula will automatically determine the value data type returned.
DAX supports the following data types:
Tabular data models also include the Table data type as the input or output to many DAX functions. For example, the FILTER function takes a table as input and outputs another table that contains only the rows that meet the filter conditions. By combining table functions with aggregation functions, you can perform complex calculations over dynamically defined data sets.
While data types are typically automatically set, it is important to understand data types and how they apply, in-particular, to DAX formulas. Errors in formulas or unexpected results, for example, are often caused by using a particular operator that cannot be used with a data type specified in an argument. For example, the formula, = 1 & 2, returns a string result of 12. The formula, = "1" + "2", however, returns an integer result of 3.
Context is an important concept to understand when creating DAX formulas. Context is what enables you to perform dynamic analysis, as the results of a formula change to reflect the current row or cell selection and also any related data. Understanding context and using context effectively are critical for building high-performing, dynamic analyses, and for troubleshooting problems in formulas.
Formulas in tabular models can be evaluated in a different context, depending on other design elements:
•Filters applied in a PivotTable or report
•Filters defined within a formula
•Relationships specified by using special functions within a formula
There are different types of context: row context, query context, and filter context.
The DAX language uses four different types of calculation operators in formulas:
•Comparison operators to compare values and return a logical TRUE\FALSE value.
•Arithmetic operators to perform arithmetic calculations that return numeric values.
•Text concatenation operators to join two or more text strings.
•Logical operators that combine two or more expressions to return a single result.
For detailed information about operators used in DAX formulas, see DAX operators.
Tables in tabular data models look like Excel tables, but are different in the way they work with data and with formulas:
•Formulas work only with tables and columns, not with individual cells, range references, or arrays.
•Formulas can use relationships to get values from related tables. The values that are retrieved are always related to the current row value.
•You cannot have irregular or "ragged" data like you can in an Excel worksheet. Each row in a table must contain the same number of columns. However, you can have empty values in some columns. Excel data tables and tabular model data tables are not interchangeable.
Mar 5, 2024 · Unleash the power of DAX queries in Power BI! This guide dives deep into using DAX Studio, from setup to writing advanced formulas, to supercharge your data analysis.
DAX Studio is a tool to write, execute, and analyze DAX queries in Power BI Designer, Power Pivot for Excel, and Analysis Services Tabular. It includes an Object Browser, query editing and execution, formula and measure editing, syntax highlighting and formatting, integrated tracing, and query execution breakdowns.
Data Analysis Expressions (DAX) is a library of functions and operators that can be combined to build formulas and expressions in Power BI, Analysis Services, and Power Pivot in Excel data models.
Mar 15, 2024 · DAX is a collection of functions, operators, and constants that can be used in a formula, or expression, to calculate and return one or more values. DAX helps you create new information from data already in your model. Why is DAX so important? It’s easy to create a new Power BI Desktop file and import some data into it.
DAX Studio is a powerful tool for working with data in Power BI, Power Pivot, and Analysis Services. It is a standalone application that can be used to connect to a wide range of data sources and execute DAX queries. . Some of the key features of DAX Studio include.
